# Algorithms

SystemDS support different Machine learning algorithms out of the box.

As an example the lm algorithm can be used as follows:

    # Import numpy and SystemDS
    import numpy as np
    from systemds.context import SystemDSContext
    from systemds.operator.algorithm import lm

    # Set a seed
    np.random.seed(0)
    # Generate matrix of feature vectors
    features = np.random.rand(10, 15)
    # Generate a 1-column matrix of response values
    y = np.random.rand(10, 1)

    # compute the weights
    with SystemDSContext() as sds:
      weights = lm(sds.from_numpy(features), sds.from_numpy(y)).compute()
      print(weights)

The output should be similar to

    [[-0.11538199]
    [-0.20386541]
    [-0.39956035]
    [ 1.04078623]
    [ 0.4327084 ]
    [ 0.18954599]
    [ 0.49858968]
    [-0.26812763]
    [ 0.09961844]
    [-0.57000751]
    [-0.43386048]
    [ 0.55358873]
    [-0.54638565]
    [ 0.2205885 ]
    [ 0.37957689]]

`systemds.operator.algorithm.``als`(X: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - X – Location to read the input matrix X to be factorized

    - rank – Rank of the factorization

    - reg – Regularization:

    - lambda – Regularization parameter, no regularization if 0.0

    - maxi – Maximum number of iterations

    - check – Check for convergence after every iteration, i.e., updating U and V once

    - thr – Assuming check is set to TRUE, the algorithm stops and convergence is declared

    - if – in loss in any two consecutive iterations falls below this threshold;

    - if – FALSE thr is ignored

  Returns

    ‘OperationNode’ containing x n matrix v

`systemds.operator.algorithm.``alsCG`(X: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - X – Location to read the input matrix X to be factorized

    - rank – Rank of the factorization

    - reg – Regularization:

    - lambda – Regularization parameter, no regularization if 0.0

    - maxi – Maximum number of iterations

    - check – Check for convergence after every iteration, i.e., updating U and V once

    - thr – Assuming check is set to TRUE, the algorithm stops and convergence is declared

    - if – in loss in any two consecutive iterations falls below this threshold;

    - if – FALSE thr is ignored

  Returns

    ‘OperationNode’ containing x n matrix v

`systemds.operator.algorithm.``alsDS`(X: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - V – Location to read the input matrix V to be factorized

    - L – Location to write the factor matrix L

    - R – Location to write the factor matrix R

    - rank – Rank of the factorization

    - lambda – Regularization parameter, no regularization if 0.0

    - maxi – Maximum number of iterations

    - check – Check for convergence after every iteration, i.e., updating L and R once

    - thr – Assuming check is set to TRUE, the algorithm stops and convergence is declared

    - if – in loss in any two consecutive iterations falls below this threshold;

    - if – FALSE thr is ignored

  Returns

    ‘OperationNode’ containing x n matrix r

`systemds.operator.algorithm.``alsTopkPredict`(userIDs: systemds.operator.nodes.matrix.Matrix, I: systemds.operator.nodes.matrix.Matrix, L: systemds.operator.nodes.matrix.Matrix, R: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - userIDs – Column vector of user-ids (n x 1)

    - I – Indicator matrix user-id x user-id to exclude from scoring

    - L – The factor matrix L: user-id x feature-id

    - R – The factor matrix R: feature-id x item-id

    - K – The number of top-K items

  Returns

    ‘OperationNode’ containing users (rows) & a matrix containing the top-k predicted ratings for the specified users (rows)

`systemds.operator.algorithm.``arima`(X: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - X – The input Matrix to apply Arima on.

    - max_func_invoc –

      ?

    - p – non-seasonal AR order

    - d – non-seasonal differencing order

    - q – non-seasonal MA order

    - P – seasonal AR order

    - D – seasonal differencing order

    - Q – seasonal MA order

    - s – period in terms of number of time-steps

    - include_mean – center to mean 0, and include in result

    - solver – solver, is either “cg” or “jacobi”

  Returns

    ‘OperationNode’ containing the calculated coefficients

`systemds.operator.algorithm.``bivar`(X: systemds.operator.nodes.matrix.Matrix, S1: systemds.operator.nodes.matrix.Matrix, S2: systemds.operator.nodes.matrix.Matrix, T1: systemds.operator.nodes.matrix.Matrix, T2: systemds.operator.nodes.matrix.Matrix, verbose: bool)
  Parameters

    verbose – Print bivar stats

  Returns

    ‘OperationNode’ containing

`systemds.operator.algorithm.``confusionMatrix`(P: systemds.operator.nodes.matrix.Matrix, Y: systemds.operator.nodes.matrix.Matrix)
  Parameters

    - P – vector of Predictions

    - Y – vector of Golden standard One Hot Encoded; the one hot encoded vector of actual labels

  Returns

    ‘OperationNode’ containing the confusion matrix sums of classifications & the confusion matrix averages of each true class

`systemds.operator.algorithm.``cox`(X: systemds.operator.nodes.matrix.Matrix, TE: systemds.operator.nodes.matrix.Matrix, F: systemds.operator.nodes.matrix.Matrix, R: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - X – Location to read the input matrix X containing the survival data

    - containing – information

    - TE – Column indices of X as a column vector which contain timestamp

    - F – Column indices of X as a column vector which are to be used for

    - fitting – model

    - R – If factors (categorical variables) are available in the input matrix

    - the – X

    - each – needs to be removed from X; in this case the start

    - and – corresponding to the baseline level need to be the same;

    - if – not provided by default all variables are considered to be continuous

    - alpha – Parameter to compute a 100*(1-alpha)% confidence interval for the betas

    - tol – Tolerance (“epsilon”)

    - moi – Max. number of outer (Newton) iterations

    - mii – Max. number of inner (conjugate gradient) iterations, 0 = no max

  Returns

    ‘OperationNode’ containing a summary of some statistics of the fitted model: & matrix rt that contains the order-preserving recoded timestamps from x & which is matrix x with sorted timestamps & matrix mf that contains the column indices of x with the baseline factors removed (if available)

`systemds.operator.algorithm.``decisionTree`(X: systemds.operator.nodes.matrix.Matrix, Y: systemds.operator.nodes.matrix.Matrix, R: systemds.operator.nodes.matrix.Matrix, verbose: bool, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - a – vector, other positive Integers indicate the number of categories

    - If – not provided by default all variables are assumed to be scale

    - bins – Number of equiheight bins per scale feature to choose thresholds

    - depth – Maximum depth of the learned tree

    - verbose – boolean specifying if the algorithm should print information while executing

  Returns

    ‘OperationNode’ containing looks at if j is an internal node, otherwise 0 & 6,7,… if j is categorical & a leaf node: number of misclassified samples reaching at node j & feature chosen for j is categorical rows 6,7,… depict the value subset chosen for j & a leaf node 1 if j is impure and the number of samples at j > threshold, otherwise 0

`systemds.operator.algorithm.``executePipeline`(X: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Returns

    ‘OperationNode’ containing encoding of categorical features & features & ohe call, to call inside eval as a function & to call inside eval as a function & doing relative over-sampling & count & replace the null with default values & version of pca

`systemds.operator.algorithm.``gaussianClassifier`(D: systemds.operator.nodes.matrix.Matrix, C: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - varSmoothing – Smoothing factor for variances

    - verbose – Print accuracy of the training set

  Returns

    ‘OperationNode’ containing

`systemds.operator.algorithm.``gmmPredict`(X: systemds.operator.nodes.matrix.Matrix, weight: systemds.operator.nodes.matrix.Matrix, mu: systemds.operator.nodes.matrix.Matrix, precisions_cholesky: systemds.operator.nodes.matrix.Matrix, model: str)
  Parameters

    - X – Matrix X (instances to be clustered)

    - weight – Weight of learned model

    - mu – fitted clusters mean

    - precisions_cholesky – fitted precision matrix for each mixture

    - model – fitted model

  Returns

    ‘OperationNode’ containing predicted cluster labels & probabilities of belongingness & for new instances given the variance and mean of fitted data

`systemds.operator.algorithm.``hospitalResidencyMatch`(R: systemds.operator.nodes.matrix.Matrix, H: systemds.operator.nodes.matrix.Matrix, capacity: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - R – Residents matrix R.

    - It – an ORDERED matrix.

    - H – Hospitals matrix H.

    - It – an UNORDRED matrix.

    - capacity – capacity of Hospitals matrix C.

    - It – a [n*1] matrix with non zero values.

    - with – and vice-versa (higher is better).

  Returns

    ‘OperationNode’ containing result matrix & result matrix & an ordered matrix, this means that resident 1 (row 1) likes hospital 2 the most, followed by hospital 1 and hospital 3. & unordered, this would mean that resident 1 (row 1) likes hospital 3 the most (since the value at [1,3] is the row max), & 1 (2.0 preference value) and hospital 2 (1.0 preference value). & an unordered matrix this means that hospital 1 (row 1) likes resident 1 the most (since the value at [1,1] is the row max). & matched with hospital 3 (since [1,3] is non-zero) at a preference level of 2.0. & matched with hospital 1 (since [2,1] is non-zero) at a preference level of 1.0. & matched with hospital 2 (since [3,2] is non-zero) at a preference level of 2.0.

`systemds.operator.algorithm.``img_cutout`(img_in: systemds.operator.nodes.matrix.Matrix, x: int, y: int, width: int, height: int, fill_value: float)
  Parameters

    - img_in – Input image as 2D matrix with top left corner at [1, 1]

    - x – Column index of the top left corner of the rectangle (starting at 1)

    - y – Row index of the top left corner of the rectangle (starting at 1)

    - width – Width of the rectangle (must be positive)

    - height – Height of the rectangle (must be positive)

    - fill_value – The value to set for the rectangle

  Returns

    ‘OperationNode’ containing output image as 2d matrix with top left corner at [1, 1]

`systemds.operator.algorithm.``img_invert`(img_in: systemds.operator.nodes.matrix.Matrix, max_value: float)
  Parameters

    - img_in – Input image

    - max_value – The maximum value pixels can have

  Returns

    ‘OperationNode’ containing

`systemds.operator.algorithm.``img_posterize`(img_in: systemds.operator.nodes.matrix.Matrix, bits: int)
  Parameters

    - img_in – Input image

    - bits – The number of bits keep for the values.

    - 1 – and white, 8 means every integer between 0 and 255.

  Returns

    ‘OperationNode’ containing

`systemds.operator.algorithm.``img_rotate`(img_in: systemds.operator.nodes.matrix.Matrix, radians: float, fill_value: float)
  Parameters

    - img_in – Input image as 2D matrix with top left corner at [1, 1]

    - radians – The value by which to rotate in radian.

    - fill_value – The background color revealed by the rotation

  Returns

    ‘OperationNode’ containing output image as 2d matrix with top left corner at [1, 1]

`systemds.operator.algorithm.``img_sample_pairing`(img_in1: systemds.operator.nodes.matrix.Matrix, img_in2: systemds.operator.nodes.matrix.Matrix, weight: float)
  Parameters

    - img_in1 – First input image

    - img_in2 – Second input image

    - weight – The weight given to the second image.

    - 0 – img_in1, 1 means only img_in2 will be visible

  Returns

    ‘OperationNode’ containing

`systemds.operator.algorithm.``img_shear`(img_in: systemds.operator.nodes.matrix.Matrix, shear_x: float, shear_y: float, fill_value: float)
  Parameters

    - img_in – Input image as 2D matrix with top left corner at [1, 1]

    - shear_x – Shearing factor for horizontal shearing

    - shear_y – Shearing factor for vertical shearing

    - fill_value – The background color revealed by the shearing

  Returns

    ‘OperationNode’ containing output image as 2d matrix with top left corner at [1, 1]

`systemds.operator.algorithm.``img_transform`(img_in: systemds.operator.nodes.matrix.Matrix, out_w: int, out_h: int, a: float, b: float, c: float, d: float, e: float, f: float, fill_value: float)
  Parameters

    - img_in – Input image as 2D matrix with top left corner at [1, 1]

    - out_w – Width of the output image

    - out_h – Height of the output image

    - abcdef – The first two rows of the affine matrix in row-major order

    - fill_value – The background of the image

  Returns

    ‘OperationNode’ containing output image as 2d matrix with top left corner at [1, 1]

`systemds.operator.algorithm.``img_translate`(img_in: systemds.operator.nodes.matrix.Matrix, offset_x: float, offset_y: float, out_w: int, out_h: int, fill_value: float)
  Parameters

    - img_in – Input image as 2D matrix with top left corner at [1, 1]

    - offset_x – The distance to move the image in x direction

    - offset_y – The distance to move the image in y direction

    - out_w – Width of the output image

    - out_h – Height of the output image

    - fill_value – The background of the image

  Returns

    ‘OperationNode’ containing output image as 2d matrix with top left corner at [1, 1]

`systemds.operator.algorithm.``km`(X: systemds.operator.nodes.matrix.Matrix, TE: systemds.operator.nodes.matrix.Matrix, GI: systemds.operator.nodes.matrix.Matrix, SI: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - X – Input matrix X containing the survival data:

    - number – (categorical features) for grouping and/or stratifying

    - TE – Column indices of X which contain timestamps (first entry) and event

    - GI – Column indices of X corresponding to the factors to be used for grouping

    - SI – Column indices of X corresponding to the factors to be used for stratifying

    - alpha – Parameter to compute 100*(1-alpha)% confidence intervals for the survivor

    - function – median

    - err_type – Parameter to specify the error type according to “greenwood” (the default) or “peto”

    - conf_type – Parameter to modify the confidence interval; “plain” keeps the lower and

    - upper – the confidence interval unmodified, “log” (the default)

    - corresponds – transformation and “log-log” corresponds to the

    - test_type – If survival data for multiple groups is available specifies which test to

    - perform – survival data across multiple groups: “none” (the default)

  Returns

    ‘OperationNode’ containing 7 consecutive columns in km corresponds to a unique combination of groups and strata in the data & schema & whose dimension depends on the number of groups (g) and strata (s) in the data (k denotes the number & for grouping ,i.e., ncol(gi) and l denotes the number of factors used for stratifying, i.e., ncol(si)) & of groups and strata is equal to 1, m will have 4 columns with & data from multiple groups available and ttype=log-rank or wilcoxon, a 1 x 4 matrix t and an g x 5 matrix t_groups_oe with

`systemds.operator.algorithm.``kmeans`(X: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - X – The input Matrix to do KMeans on.

    - k – Number of centroids

    - runs – Number of runs (with different initial centroids)

    - max_iter – Maximum number of iterations per run

    - eps – Tolerance (epsilon) for WCSS change ratio

    - is_verbose – do not print per-iteration stats

    - avg_sample_size_per_centroid – Average number of records per centroid in data samples

    - seed – The seed used for initial sampling. If set to -1 random seeds are selected.

  Returns

    ‘OperationNode’ containing the mapping of records to centroids & the output matrix with the centroids

`systemds.operator.algorithm.``kmeansPredict`(X: systemds.operator.nodes.matrix.Matrix, C: systemds.operator.nodes.matrix.Matrix)
  Parameters

    - X – The input Matrix to do KMeans on.

    - C – The input Centroids to map X onto.

  Returns

    ‘OperationNode’ containing the mapping of records to centroids

`systemds.operator.algorithm.``l2svm`(X: systemds.operator.nodes.matrix.Matrix, Y: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - X – matrix X of feature vectors

    - Y – matrix Y of class labels have to be a single column

    - intercept – No Intercept ( If set to TRUE then a constant bias column is added to X)

    - epsilon – Procedure terminates early if the reduction in objective function value is less than epsilon (tolerance) times the initial objective function value.

    - lambda – Regularization parameter (lambda) for L2 regularization

    - maxIterations – Maximum number of conjugate gradient iterations

    - maxii –

    - verbose – Set to true if one wants print statements updating on loss.

    - columnId – The column Id used if one wants to add a ID to the print statement, Specificly usefull when L2SVM is used in MSVM.

  Returns

    ‘OperationNode’ containing model matrix

`systemds.operator.algorithm.``l2svmPredict`(X: systemds.operator.nodes.matrix.Matrix, W: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - X – matrix X of feature vectors to classify

    - W – matrix of the trained variables

    - verbose – Set to true if one wants print statements.

  Returns

    ‘OperationNode’ containing classification labels maxed to ones and zeros.

`systemds.operator.algorithm.``lasso`(X: systemds.operator.nodes.matrix.Matrix, y: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - X – input feature matrix

    - y – matrix Y columns of the design matrix

    - tol – target convergence tolerance

    - M – history length

    - tau – regularization component

    - maxi – maximum number of iterations until convergence

  Returns

    ‘OperationNode’ containing

`systemds.operator.algorithm.``lm`(X: systemds.operator.nodes.matrix.Matrix, y: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - X – Matrix of feature vectors.

    - y – 1-column matrix of response values.

    - icpt – Intercept presence, shifting and rescaling the columns of X

    - reg – Regularization constant (lambda) for L2-regularization. set to nonzero for highly dependant/sparse/numerous features

    - tol – Tolerance (epsilon); conjugate gradient procedure terminates early if L2 norm of the beta-residual is less than tolerance * its initial norm

    - maxi – Maximum number of conjugate gradient iterations. 0 = no maximum

    - verbose – If TRUE print messages are activated

  Returns

    ‘OperationNode’ containing the model fit

`systemds.operator.algorithm.``msvmPredict`(X: systemds.operator.nodes.matrix.Matrix, W: systemds.operator.nodes.matrix.Matrix)
  Parameters

    - X – matrix X of feature vectors to classify

    - W – matrix of the trained variables

  Returns

    ‘OperationNode’ containing classification labels maxed to ones and zeros.

`systemds.operator.algorithm.``multiLogReg`(X: systemds.operator.nodes.matrix.Matrix, Y: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - X – Location to read the matrix of feature vectors

    - Y – Location to read the matrix with category labels

    - icpt – Intercept presence, shifting and rescaling X columns: 0 = no intercept, no shifting, no rescaling; 1 = add intercept, but neither shift nor rescale X; 2 = add intercept, shift & rescale X columns to mean = 0, variance = 1

    - tol – tolerance (“epsilon”)

    - reg – regularization parameter (lambda = 1/C); intercept is not regularized

    - maxi – max. number of outer (Newton) iterations

    - maxii – max. number of inner (conjugate gradient) iterations, 0 = no max

    - verbose – flag specifying if logging information should be printed

  Returns

    ‘OperationNode’ containing betas as output for prediction

`systemds.operator.algorithm.``multiLogRegPredict`(X: systemds.operator.nodes.matrix.Matrix, B: systemds.operator.nodes.matrix.Matrix, Y: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - X – Data Matrix X

    - B – Regression parameters betas

    - Y – Response vector Y

    - verbose –

      /

  Returns

    ‘OperationNode’ containing matrix m of predicted means/probabilities & predicted response vector & scalar value of accuracy

`systemds.operator.algorithm.``pca`(X: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - X – Input feature matrix

    - K – Number of reduced dimensions (i.e., columns)

    - Center – Indicates whether or not to center the feature matrix

    - Scale – Indicates whether or not to scale the feature matrix

  Returns

    ‘OperationNode’ containing output dominant eigen vectors (can be used for projections) & the column means of the input, subtracted to construct the pca & the scaling of the values, to make each dimension same size.

`systemds.operator.algorithm.``ppca`(X: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - X – n x m input feature matrix

    - k – indicates dimension of the new vector space constructed from eigen vectors

    - maxi – maximum number of iterations until convergence

    - tolobj – objective function tolerance value to stop ppca algorithm

    - tolrecerr – reconstruction error tolerance value to stop the algorithm

    - verbose – verbose debug output

  Returns

    ‘OperationNode’ containing output feature matrix with k columns & output dominant eigen vectors (can be used for projections)

`systemds.operator.algorithm.``randomForest`(X: systemds.operator.nodes.matrix.Matrix, Y: systemds.operator.nodes.matrix.Matrix, R: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - X – Feature matrix X; note that X needs to be both recoded and dummy coded

    - Y – Label matrix Y; note that Y needs to be both recoded and dummy coded

    - R – ” Matrix which for each feature in X contains the following information

    - If – not provided by default all variables are assumed to be scale

    - bins – Number of equiheight bins per scale feature to choose thresholds

    - depth – Maximum depth of the learned tree

    - num_leaf – Number of samples when splitting stops and a leaf node is added

    - num_samples – Number of samples at which point we switch to in-memory subtree building

    - num_trees – Number of trees to be learned in the random forest model

    - subsamp_rate – Parameter controlling the size of each tree in the forest; samples are selected from a

    - Poisson – parameter subsamp_rate (the default value is 1.0)

    - feature_subset – Parameter that controls the number of feature used as candidates for splitting at each tree node

    - as – of number of features in the dataset;

    - by – root of features (i.e., feature_subset = 0.5) are used at each tree node

    - impurity – Impurity measure: entropy or Gini (the default)

  Returns

    ‘OperationNode’ containing tree and each row contains the following information: & that leaf node j is supposed to predict & 7,8,… if j is categorical & chosen for j is categorical rows 7,8,… depict the value subset chosen for j & c containing the number of times samples are chosen in each tree of the random forest & from scale feature ids to global feature ids & from categorical feature ids to global feature ids

`systemds.operator.algorithm.``stableMarriage`(P: systemds.operator.nodes.matrix.Matrix, A: systemds.operator.nodes.matrix.Matrix, **kwargs: Dict[str, Union[DAGNode, str, int, float, bool]])
  Parameters

    - P – proposer matrix P.

    - It – a square matrix with no zeros.

    - A – acceptor matrix A.

    - It – a square matrix with no zeros.

    - ordered – If true, P and A are assumed to be ordered,

    - index – vice-versa (higher is better).

  Returns

    ‘OperationNode’ containing result matrix & 1 (2.0 preference value) and acceptor 2 (1.0 preference value). & 3 (2.0 preference value) and proposer 2 (1.0 preference value). & matched with proposer 3 (since [1,3] is non-zero) at a preference level of 3.0. & matched with proposer 2 (since [2,2] is non-zero) at a preference level of 3.0. & matched with proposer 1 (since [3,1] is non-zero) at a preference level of 1.0.

`systemds.operator.algorithm.``toOneHot`(X: systemds.operator.nodes.matrix.Matrix, numClasses: int)
  Parameters

    - X – vector with N integer entries between 1 and numClasses

    - numclasses – number of columns, must be >= largest value in X

  Returns

    ‘OperationNode’ containing matrix with shape (n, numclasses)

`systemds.operator.algorithm.``tomeklink`(X: systemds.operator.nodes.matrix.Matrix, y: systemds.operator.nodes.matrix.Matrix)
  Parameters

    - X – Data Matrix (nxm)

    - y – Label Matrix (nx1)

  Returns

    ‘OperationNode’ containing